PCA [1]}, [2]}, [3]} is a traditional unsupervised technique for dimensionality reduction.
The method linearly transforms the data points into a new coordinate system in which the new coordinates are the principal components, which are typically obtained from a SVD of the data matrix.
We use the first and section principal component for projecting the data into two dimensions because these components describe the features of the highest explained variance.
We use the PCA function from the decomposition module of Scikit-learn (sklearn) [4]}.
